Description

Key Responsibilities

Design and implement enterprise-scale Agentic AI solutions using modern multi-agent orchestration frameworks.

Architect AI systems leveraging Large Language Models (LLMs), Generative AI, and advanced prompt and context engineering techniques.

Build scalable, cloud-native AI applications using Microsoft Azure services.

Develop intelligent workflows utilizing vector embeddings, retrieval-augmented generation (RAG), and AI orchestration patterns.

Design secure, resilient, and highly available microservices-based AI platforms.

Collaborate with cross-functional engineering, architecture, and business teams to deliver innovative AI solutions.

Optimize AI model performance, scalability, reliability, and operational efficiency.

Ensure solutions align with enterprise security, governance, and compliance standards.

Mentor technical teams and provide architectural leadership throughout the software development lifecycle.

Required Skills & Qualifications

13+ years of overall IT experience with strong expertise in enterprise solution architecture.

Hands-on experience with Agentic AI frameworks, A2A frameworks, and MCP protocol for multi-agent orchestration.

Strong expertise in Generative AI, Large Language Models (LLMs), Natural Language Models (NLMs), vector embeddings, prompt engineering, context engineering, and LLM fine-tuning.

Advanced Python programming skills; experience with Java or Go is an added advantage.

Proven experience deploying, monitoring, and scaling AI solutions on Microsoft Azure.

Strong knowledge of Azure AI Search, Azure Cosmos DB, Redis, and Azure Blob Storage.

Experience with Apache Iceberg is a plus.

Strong understanding of cloud-native architecture, microservices, containerization, serverless computing, and distributed systems.

Experience designing highly scalable, secure, and production-ready AI platforms.

Knowledge of AI governance, security, and compliance best practices.

Healthcare or other regulated industry experience is highly preferred.

Strong communication, leadership, and stakeholder management skills.

Preferred Qualifications

Experience with enterprise AI platform architecture and AI operations (AIOps/MLOps).

Familiarity with Retrieval-Augmented Generation (RAG) architectures.

Experience integrating AI services into enterprise applications and APIs.

Knowledge of modern DevOps, CI/CD, Kubernetes, and container orchestration technologies

Key Skills

Education

Bachelor's degree